Computational procedures for probing interactions in OLS and logistic regression: SPSS and SAS implementations.
Andrew F. Hayes,Jörg Matthes +1 more
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The familiar pick-a-point approach and the much less familiar Johnson-Neyman technique for probing interactions in linear models are described and macros for SPSS and SAS are introduced to simplify the computations and facilitate the probing of interactions in ordinary least squares and logistic regression.Abstract:
Researchers often hypothesize moderated effects, in which the effect of an independent variable on an outcome variable depends on the value of a moderator variable. Such an effect reveals itself statistically as an interaction between the independent and moderator variables in a model of the outcome variable. When an interaction is found, it is important to probe the interaction, for theories and hypotheses often predict not just interaction but a specific pattern of effects of the focal independent variable as a function of the moderator. This article describes the familiar pick-a-point approach and the much less familiar Johnson-Neyman technique for probing interactions in linear models and introduces macros for SPSS and SAS to simplify the computations and facilitate the probing of interactions in ordinary least squares and logistic regression. A script version of the SPSS macro is also available for users who prefer a point-and-click user interface rather than command syntax.read more
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References
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Book
Applied multiple regression/correlation analysis for the behavioral sciences
TL;DR: In this article, the Mathematical Basis for Multiple Regression/Correlation and Identification of the Inverse Matrix Elements is presented. But it does not address the problem of missing data.
Book
Multiple Regression: Testing and Interpreting Interactions
Leona S. Aiken,Stephen G. West +1 more
TL;DR: In this article, the effects of predictor scaling on the coefficients of regression equations are investigated. But, they focus mainly on the effect of predictors scaling on coefficients of regressions.
Journal ArticleDOI
Computational Tools for Probing Interactions in Multiple Linear Regression, Multilevel Modeling, and Latent Curve Analysis.
TL;DR: The authors provides an overview of methods used to probe interaction effects and describes a unified collection offreely available online resources that researchers can use to obtain significance tests and confidence bands for simple slopes across the range of the moderator in the MLR, HLM, and LCA contexts.
Book
Interaction effects in multiple regression
TL;DR: In this article, the authors present a survey of the existing literature on the analysis of moderated relationships involving continuous variables, focusing on analyzing interaction effects in the context of multiple regression and structural equation analyses.
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